Wheat yield estimation in Russia with modis time-series data based on light use efficiency model

2013 ◽  
Author(s):  
Xin Du ◽  
Jihua Meng ◽  
Igor Savin ◽  
Qiangzi Li
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Wheat Yield ◽  
Series Data ◽  
Light Use Efficiency ◽  
Yield Estimation ◽  
Use Efficiency ◽  
Light Use Efficiency Model

scholarly journals EFFICIENCY AND EFFECTIVENESS OF USING VILLAGE INCOME AND EXPENDITURE BUDGET (APBDesa) (Efisiensi Dan Efektifitas Penggunaan Anggaran Pendapatan Dan Belanja Desa(APBDesa) )

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Kalzum R Jumiyanti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Efficiency Ratio ◽  
Efficiency And Effectiveness ◽  
Use Efficiency ◽  
To Come

Abstract This research was conducted with the aim of analyzing the Efficiency and Effectiveness of the Use of Village Budget (APBDes) in Tamaila Village, Tolangohula District, Gorontalo Regency.The method of analysis used in this study is to use Efficiency Ratio that is by comparing with the realization of village expenditure and the target of village income by using time series data between 2013-2016 and using the Effectiveness Ratio which compares with Realiation of Village Original Revenue and Village Original Revenue Target by using time series data between 2013-2017.The results of the study show that the value of income effectiveness is very good in Tamaila Village starting in 2012-2016 which is indicated by the ratio between 90-100% and even exceeding 100%. And the hope in the year to come can be improved and managed to be achieved according to the desired target. The value of Village spending efficiency is very good in Tamaila Village starting in 2012-2016 which is indicated by the ratio between 90-100%. Although the 2015 value of the ratio decreased by only around 86%, in the coming year it is expected that this value will be increased. Abstrak Penelitian ini dilakukan dengan tujuan Untuk menganalisis Efisiensi dan Efektifitas Penggunan Anggaran Pendapatan dan Belaja Desa  (APBDes) di Desa Tamaila Kecamatan Tolangohula Kabupaten Gorontalo.Metode Analisis yang digunakan dalam penelitian ini adalah menggunakan Ratio Efisiensi yaitu dengan membadingkan  dengan realisasi belanja desa dan target Pendapatan desa dengan menggunakan time series data antara tahun 2013-2016 dan menggunakan Ratio Efektivitas yaitu membandingkan dengan Realiasi Pendapatan Asli Desa dan Target Pendapatan Asli Desa dengan menggunakan time series data antara tahun 2013-2017.Hasil Penelitian menujukkan bahwa nilai efektifitas pendapatan  sangat baik di Desa Tamaila mulai tahun 2012-2016 yang ditujukkan oleh nilai rationya antara 90-100 % bahkan melampaui 100 %. Dan harapannya ditahun akan datang agar dapat ditingkatkan dan diusahakan dapat tercapai sesuai target yang diinginkan. Nilai efisiensi belanja Desa  sangat baik di Desa Tamaila mulai tahun 2012-2016 yang ditujukkan oleh nilai rationya antara 90-100 %.  Walaupun tahun 2015 mengalami penurunan nilai ratio yakni hanya berkisar 86 % sehingga ditahun akan datang diharapkan hal ini perlu ditingkatkan nilai peruntukkannya

scholarly journals Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 768 ◽  
Author(s):  
Rachna Jain ◽  
Nikita Jain ◽  
Shivani Kapania ◽  
Le Son
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Yield Production ◽  
Fuzzy Partitioning ◽  
Predicted Values ◽  
Mean Function

Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

scholarly journals Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

2018 ◽  
Vol 10 (10) ◽  
pp. 1659 ◽  
Author(s):  
Inbal Becker-Reshef ◽  
Belen Franch ◽  
Brian Barker ◽  
Emilie Murphy ◽  
Andres Santamaria-Artigas ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Series Data ◽  
Crop Type ◽  
Winter Wheat Yield

Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

scholarly journals A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1524
Author(s):  
Saïd Khabba ◽  
Salah Er-Raki ◽  
Jihad Toumi ◽  
Jamal Ezzahar ◽  
Bouchra Ait Hssaine ◽  
...  
Keyword(s):  
Simple Model ◽  
Harvest Index ◽  
Wheat Yield ◽  
Growth And Yield ◽  
Light Use Efficiency ◽  
Proposed Model ◽  
Aquacrop Model ◽  
Use Efficiency ◽  
Semi Arid ◽  
Light Use Efficiency Model

In this study, a simple model, based on a light-use-efficiency model, was developed in order to estimate growth and yield of the irrigated winter wheat under semi-arid conditions. The originality of the proposed method consists in (1) the modifying of the expression of the conversion coefficient (εconv) by integrating an appropriate stress threshold (ksconv) for triggering irrigation, (2) the substitution of the product of the two maximum coefficients of interception (εimax) and conversion (εconv_max) by a single parameter εmax, (3) the modeling of εmax as a function of the Cumulative Growing Degree Days (CGDD) since sowing date, and (4) the dynamic expression of the harvest index (HI) as a function of the CGDD and the final harvest index (HI0) depending on the maximum value of the Normalized Difference Vegetation Index (NDVI). The calibration and validation of the proposed model were performed based on the observations of wheat dry matter (DM) and grain yield (GY) which were collected on the R3 irrigated district of the Haouz plain (center of Morocco), during three agricultural seasons. Further, the outputs of the simple model were also evaluated against the AquaCrop model estimates. The model calibration allowed the parameterization of εmax in four periods according to the wheat phenological stages. By contrast, a linear evolution was sufficient to represent the relationship between HI and CGDD. For the model validation, the obtained results showed a good agreement between the estimated and observed values with a Root Mean Square Error (RMSE) of about 1.07 and 0.57 t/ha for DM and GY, respectively. These correspond to a relative RMSE of about 19% for DM and 20% for GY. Likewise, although of its simplicity, the accuracy of the proposed model seems to be comparable to that of the AquaCrop model. For GY, R2, and RMSE values were respectively 0.71 and 0.44 t/ha for the developed approach and 0.88 and 0.37 t/ha for AquaCrop. Thus, the proposed simple light-use-efficiency model can be considered as a useful tool to correctly reproduce DM and GY values.

scholarly journals WHEAT YIELD PREDICTION BASED ON MODIS NDVI TIME SERIES DATA IN THE WIDER REGION OF A CEREAL PROCESSING PLANT

2019 ◽  
Vol 9 (2) ◽  
pp. 193-202
Author(s):  
Andrea Szabó ◽  
◽  
János Tamás ◽  
Odunayo Adeniyi David ◽  
Attila Nagy ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Wheat Yield ◽  
Series Data ◽  
Processing Plant ◽  
Yield Prediction ◽  
Modis Ndvi ◽  
Ndvi Time Series

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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